Publications
254 results found
Alrajeh D, Kramer J, Russo A, et al., 2011, An Inductive approach for modal transition system refinement, Pages: 106-116, ISSN: 1868-8969
Modal Transition Systems (MTSs) provide an appropriate framework for modelling software behaviour when only a partial specification is available. A key characteristic of an MTS is that it explicitly models events that a system is required to provide and is proscribed from exhibiting, and those for which no specification is available, called maybe events. Incremental elaboration of maybe events into either required or proscribed events can be seen as a process of MTS refinement, resulting from extending a given partial specification with more information about the system behaviour. This paper focuses on providing automated support for computing strong refinements of an MTS with respect to event traces that describe required and proscribed behaviours using a non-monotonic inductive logic programming technique. A real case study is used to illustrate the practical application of the approach.
Corapi D, Russo A, 2011, ASPAL. Proof of soundness and completeness, Departmental Technical Report: 11/5, Publisher: Department of Computing, Imperial College London, 11/5
We provide here a brief introduction and proof of soundnessand completeness of the ILP system ASPAL. This document is in supportof our ICLP 2011 submission, for the reviewers' bene ts.
Craven, Lobo J, Lupu E, et al., 2011, Policy Refinement: Decomposition and Operationalization for Dynamic Domains, 7th IEEE Int. Conference on Network and Service Management (CNSM 2011), Publisher: IEEE
We describe a method for policy refinement. The refinement process involves stages of decomposition, operational- ization, deployment and re-refinement, and operates on policies expressed in a logical language flexible enough to be translated into many different enforceable policy dialects. We illustrate with examples from a coalition scenario, and describe how the stages of decomposition and operationaliztion work internally, and fit together in an interleaved fashion. Domains are represented in a logical formalization of UML diagrams. Both authorization and obligation policies are supported
Martinelli F, Olmedilla D, Russo A, 2011, Proceedings - 2011 IEEE International Symposium on Policies for Distributed Systems and Networks, POLICY 2011: Preface
Corapi D, Russo A, De Vos M, et al., 2011, Normative design using inductive learning, THEORY AND PRACTICE OF LOGIC PROGRAMMING, Vol: 11, Pages: 783-799, ISSN: 1471-0684
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- Citations: 14
Russo AM, Rodrigues O, Gabbay D, et al., 2011, Belief Revision, Handbook of Philosophical Logic, Editors: Gabbay, Franz, Publisher: Springer Netherlands, Pages: 1-114
Ma J, Russo A, Broda K, et al., 2011, Multi-agent abductive reasoning with confidentiality, Pages: 1071-1072
In the context of multi-agent hypothetical reasoning, agents typically have partial knowledge about their environments, and the union of such knowledge is still incomplete to represent the whole world. Thus, given a global query they need to collaborate with each other to make correct inferences and hypothesis, whilst maintaining global constraints. There are many real world applications in which the confidentiality of agent knowledge is of primary concern, and hence the agents may not share or communicate all their information during the collaboration. This extra constraint gives a new challenge to multi-agent reasoning. This paper shows how this dichotomy between "open communication" in collaborative reasoning and protection of confidentiality can be accommodated, by extending a general-purpose distributed abductive logic programming system for multi-agent hypothetical reasoning with confidentiality. Specifically, the system computes consistent conditional answers for a query over a set of distributed normal logic programs with possibly unbound domains and arithmetic constraints, preserving the private information within the logic programs. Copyright © 2011, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
Corapi D, Russo A, De Vos M, et al., 2011, Norm refinement and design through inductive learning, Coordination, Organizations, Institutions, and Norms in Agent Systems VI, Revised Selected Papers, Pages: 77-94, ISSN: 0302-9743
In the physical world, the rules governing behaviour are debugged by observing an outcome that was not intended and the addition of new constraints to prevent the attainment of that outcome. We propose a similar approach to support the incremental development of normative frameworks (also called institutions) and demonstrate how this works through the validation and synthesis of normative rules using model generation and inductive learning. This is achieved by the designer providing a set of use cases, comprising collections of event traces that describe how the system is used along with the desired outcome with respect to the normative framework. The model generator encodes the description of the current behaviour of the system. The current specification and the traces for which current behaviour and expected behaviour do not match are given to the learning framework to propose new rules that revise the existing norm set in order to inhibit the unwanted behaviour. The elaboration of a normative system can then be viewed as a semi-automatic, iterative process for the detection of incompleteness or incorrectness of the existing normative rules, with respect to desired properties, and the construction of potential additional rules for the normative system. © 2011 Springer-Verlag.
Lobo J, Ma J, Russo A, et al., 2011, Refinement of History-Based Policies., Editors: Balduccini, Son, Publisher: Springer, Pages: 280-299, ISBN: 978-3-642-20831-7
We propose an efficient method to evaluate a large class of history-based policies written as logic programs. To achieve this, we dy- namically compute, from a given policy set, a finite subset of the history required and sufficient to evaluate the policies. We maintain this history by monitoring rules and transform the policies into a non history-based form. We further formally prove that evaluating history-based policies can be reduced to an equivalent, but more efficient, evaluation of the non history-based policies together with the monitoring rules.
Ma J, Russo A, Lupu E, et al., 2011, Multi-agent Confidential Abductive Reasoning, 27th International COnference on Logic Programming
Corapi D, Sykes D, Inoue K, et al., 2011, Probabilistic Rule Learning in Nonmonotonic Domains, 12th International Workshop on Computational Logic in Multi-Agent Systems (CLIMA) / 22nd International Joint Conference on Artificial Intelligence, Publisher: SPRINGER-VERLAG BERLIN, Pages: 243-258, ISSN: 0302-9743
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- Citations: 8
Ahmad M, Alexandrou I, Al-Nuaimy W, et al., 2010, WCE 2010 - World Congress on Engineering 2010: Preface, WCE 2010 - World Congress on Engineering 2010, Vol: 2
Corapi D, De Vos M, Padget J, et al., 2010, Norm refinement and design through inductive learning, Pages: 33-48, ISSN: 1613-0073
In the physical world, the rules governing behaviour are debugged by observing an outcome that was not intended and the addition of new constraints intended to prevent the attainment of that outcome. We propose a similar approach to support the incremental development of normative frameworks (also called institutions) and demonstrate how this works through the validation and synthesis of normative rules using model generation and inductive learning. This is achieved by the designer providing a set of use cases, comprising collections of event traces that describe how the system is used along with the desired outcome with respect to the normative framework. The model generator encodes the description of the current behaviour of the system. The current specification and the traces for which current behaviour and expected behaviour do not match are given to the learning framework to propose new rules that revise the existing norm set in order to inhibit the unwanted behaviour. The elaboration of a normative system can then be viewed as a semi-automatic, iterative process for the detection of incompleteness or incorrectness of the existing normative rules, with respect to desired properties, and the construction of potential additional rules for the normative system.
Craven R, Lobo J, Lupu EC, et al., 2010, Decomposition techniques for policy refinement., 6th Int. Conference on Network and Service Management, Publisher: IEEE, Pages: 72-79
The automation of policy refinement, whilst promis- ing great benefits for policy-based management, has hitherto received relatively little treatment in the literature, with few concrete approaches emerging. In this paper we present initial steps towards a framework for automated distributed policy refinement for both obligation and authorization policies. We present examples drawn from military scenarios, describe details of our formalism and methods for action decomposition, and discuss directions for future research.
Gabbay DM, Rodrigues OT, Russo A, 2010, Cognitive Technologies: Preface, Cognitive Technologies, ISSN: 1611-2482
Gabbay D, Rodrigues O, Russo A, 2010, Revision, Acceptability and Context: Theoretic and Algorithmic Aspects, Publisher: Springer Verlag, ISBN: 978-3-642-14158-4
Alrajeh D, Kramer J, Russo A, et al., 2010, Deriving non-Zeno behaviour models from goal models using ILP, FORMAL ASPECTS OF COMPUTING, Vol: 22, Pages: 217-241, ISSN: 0934-5043
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- Citations: 7
Calo S, Karat J, Lobo J, et al., 2010, Policy Technologies for Security Management in Coalition Networks, Network Science for Military Coalition Operations: Information Exchange and Interaction, Publisher: Information Science Reference, Pages: 146-173, ISBN: 978-1-61520-855-5
Gabbay DM, Rodrigues OT, Russo A, 2010, Conclusions and discussions, Pages: 359-375, ISSN: 1611-2482
In this book we provided a comprehensive set of methodologies to tackle the problem of belief revision. The methodologies determine acceptability criteria for the result of the revision operation based on the context in which the operation takes place. In what follows, we summarise the main points raised in the book and we end with some discussions in Section 9.2. © 2010 Springer-Verlag Berlin Heidelberg.
Dickens L, Broda K, Russo A, 2010, The Dynamics of Multi-Agent Reinforcement Learning, ECAI 2010 - 19TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE, Pages: 367-372, ISSN: 0922-6389
Ma J, Russo A, Broda K, et al., 2010, On the Implementation of Speculative Constraint Processing, Workshop on Computational Logic in Multi-Agent Systems
Ma J, Russo A, Broda K, et al., 2010, Distributed Abductive Reasoning with Constraints, 9th Conference on Autonomous Agents and Multi-Agent Systems (AAMAS10)
Ma J, Broda K, Russo A, et al., 2010, Distributed Abductive Reasoning with Constraints., Publisher: Springer, Pages: 148-166
Gabbay DM, Rodrigues OT, Russo A, 2010, Background and Overview, Publisher: SPRINGER-VERLAG BERLIN, Pages: 1-12, ISSN: 1611-2482
Gabbay DM, Rodrigues OT, Russo A, 2010, Structured Revision: Non-linear Methods for Information Change, Publisher: SPRINGER-VERLAG BERLIN, Pages: 139-176, ISSN: 1611-2482
Gabbay DM, Rodrigues OT, Russo A, 2010, Introducing Revision Theory, Publisher: SPRINGER-VERLAG BERLIN, Pages: 13-54, ISSN: 1611-2482
Gabbay DM, Rodrigues OT, Russo A, 2010, Stepwise Revision Operations, Publisher: SPRINGER-VERLAG BERLIN, Pages: 55-103, ISSN: 1611-2482
Gabbay DM, Rodrigues OT, Russo A, 2010, Algorithmic Context Revision, Publisher: SPRINGER-VERLAG BERLIN, Pages: 177-222, ISSN: 1611-2482
Gabbay DM, Rodrigues OT, Russo A, 2010, Object-Level Deletion, Publisher: SPRINGER-VERLAG BERLIN, Pages: 271-358, ISSN: 1611-2482
Gabbay DM, Rodrigues OT, Russo A, 2010, Iterating Revision, Publisher: SPRINGER-VERLAG BERLIN, Pages: 105-137, ISSN: 1611-2482
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